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BY: ALI AJORIAN ISFAHAN UNIVERSITY OF TECHNOLOGY 2012 GPU Architecture 1
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Age of parallelism Single CPU performance Doubled every 2 years for 30 years until 5 years ago. Marginal improvement in the last 5 years. 2005 year and checking walls Memory Wall Power Wall Processor Design Complexity Sequential or parallel: this is the problem!!! More cores rather than more clock rate 2
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Early parallel computing It was not a big idea Main frames and super computers 3
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And now GPUs Stands for “Graphics Processing Unit” Integration Scheme: a card on the motherboard with Massively Parallel computing power 4
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A desktop supper computer 5
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History of parallel computing 6
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GPUs: A Brief History Stage0: graphic accelerators Early VGA cards accelerate 2D GUI Just configurable Stage1: Fixed Graphics Hardware Graphics-only platform Very limited programmability Stage2: GPGPU Trick GPU to do general purpose computing Programmable, but requires knowledge on computer graphics Stream Processing Platforms High-level programming interface No knowledge on Computer Graphics is required Examples: NVIDIA’s CUDA, OpenCL 7
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Stream Processing Characteristics Fairly simple computation on huge amount of data (streams) Single Program Multiple Data (SPMD) Data Parallelism e.g., Matrix Operations, Image Processing 8
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Graphic accelerators to CUDA GPUs(cont) 9
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CUDA programming model CPU + GPU heterogeneous programming Applications with sequential and parallel parts Host : CPU Sequential threads Device : GPU Parallel threads in SIMT architecture some kernels that runs on a grid of threads. 10
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CUDA programming model 11
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CUDA programming model(cont) 12
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GPU Architecture (NVIDIA) 13
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GPU Architecture (Fermi) 14
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SM architecture 15
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CUDA programming model 16
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Memory types Per block registers shared memory Per thread local memory Per grid Global memory Constant memory Texture memory 17
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Memory types(cont) 18
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Questions? 19
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